‘On the face of it’: the use of automatic facial coding to understand normative feedback interventions’ emotional tax

Personalised normative feedback (PNF) is a form of social comparison intervention that is frequently applied across policy domains and in smartphone applications. However, despite the increasingly widespread adoption of this intervention typology, little is known about its detrimental emotional effects.In this large within-subject online experiment, we applied automatic facial coding to measure the short-run emotional impact of three PNF interventions similar to those implemented in the ‘wild’. Using pre-registered mixed-effects logistic regression models, we observed that the receipt of information that one’s behaviour is more negative than the norm is associated with more visible negative emotion. Moderation analysis suggested that these effects are no more pronounced for those who are sensitive to social comparison information, nor those who consider the interventions’ policy domains to be implicitly important.Notably, the evidence provided by this study may enhance social planners’ appreciation of the potential harmful impacts of PNF interventions, including distributional effects. Such enhanced appreciation may lead to more informed decisions about the situations in which PNF should or should not be implemented. Nonetheless, additional research is required to augment understanding of the psychological and demographic determinants of individual heterogeneity in emotional response to PNF interventions.